Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations499
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.8 KiB
Average record size in memory188.5 B

Variable types

Numeric23
Categorical3

Alerts

COSTE_MTO_CORR_MEDIO_POR_ORDEN is highly overall correlated with COSTE_MTO_MEDIO_POR_ORDENHigh correlation
COSTE_MTO_CORR_TOTAL is highly overall correlated with COSTE_MTO_TOTAL and 5 other fieldsHigh correlation
COSTE_MTO_MEDIO_POR_ORDEN is highly overall correlated with COSTE_MTO_CORR_MEDIO_POR_ORDEN and 1 other fieldsHigh correlation
COSTE_MTO_PREV_MEDIO_POR_ORDEN is highly overall correlated with COSTE_MTO_MEDIO_POR_ORDENHigh correlation
COSTE_MTO_PREV_TOTAL is highly overall correlated with COSTE_MTO_TOTAL and 4 other fieldsHigh correlation
COSTE_MTO_TOTAL is highly overall correlated with COSTE_MTO_CORR_TOTAL and 7 other fieldsHigh correlation
DIAS_ENTRE_FALL_CONSEC is highly overall correlated with COSTE_MTO_CORR_TOTAL and 5 other fieldsHigh correlation
DURACION_HORAS_CORR_MEDIO_POR_ORDEN is highly overall correlated with DURACION_HORAS_MEDIO_POR_ORDENHigh correlation
DURACION_HORAS_CORR_TOTAL is highly overall correlated with COSTE_MTO_CORR_TOTAL and 5 other fieldsHigh correlation
DURACION_HORAS_MEDIO_POR_ORDEN is highly overall correlated with DURACION_HORAS_CORR_MEDIO_POR_ORDEN and 1 other fieldsHigh correlation
DURACION_HORAS_PREV_MEDIO_POR_ORDEN is highly overall correlated with DURACION_HORAS_MEDIO_POR_ORDENHigh correlation
DURACION_HORAS_PREV_TOTAL is highly overall correlated with COSTE_MTO_PREV_TOTAL and 3 other fieldsHigh correlation
DURACION_HORAS_TOTAL is highly overall correlated with COSTE_MTO_CORR_TOTAL and 8 other fieldsHigh correlation
Ordenes_Correctivo is highly overall correlated with COSTE_MTO_CORR_TOTAL and 5 other fieldsHigh correlation
Ordenes_Preventivo is highly overall correlated with COSTE_MTO_PREV_TOTAL and 4 other fieldsHigh correlation
Total_Ordenes is highly overall correlated with COSTE_MTO_CORR_TOTAL and 8 other fieldsHigh correlation
ID_Equipo is uniformly distributed Uniform
ID_Equipo has unique values Unique
MEDIA_HORAS_OPERATIVAS has unique values Unique
COSTE_MTO_CORR_MEDIO_POR_ORDEN has unique values Unique
COSTE_MTO_CORR_TOTAL has unique values Unique
COSTE_MTO_PREV_MEDIO_POR_ORDEN has unique values Unique
COSTE_MTO_PREV_TOTAL has unique values Unique
COSTE_MTO_MEDIO_POR_ORDEN has unique values Unique
COSTE_MTO_TOTAL has unique values Unique

Reproduction

Analysis started2025-02-19 11:37:25.793996
Analysis finished2025-02-19 11:39:26.330006
Duration2 minutes and 0.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID_Equipo
Real number (ℝ)

Uniform  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250
Minimum1
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:26.488420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.9
Q1125.5
median250
Q3374.5
95-th percentile474.1
Maximum499
Range498
Interquartile range (IQR)249

Descriptive statistics

Standard deviation144.19316
Coefficient of variation (CV)0.57677263
Kurtosis-1.2
Mean250
Median Absolute Deviation (MAD)125
Skewness0
Sum124750
Variance20791.667
MonotonicityStrictly increasing
2025-02-19T11:39:26.832366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
329 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
335 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%

MEDIA_TEMP
Real number (ℝ)

Distinct498
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.546106
Minimum79.431429
Maximum121.92562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:27.091504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum79.431429
5-th percentile87.200842
Q194.855779
median99.886111
Q3104.39443
95-th percentile110.35105
Maximum121.92562
Range42.494196
Interquartile range (IQR)9.5386522

Descriptive statistics

Standard deviation7.0134066
Coefficient of variation (CV)0.070453852
Kurtosis-0.019873963
Mean99.546106
Median Absolute Deviation (MAD)4.9623099
Skewness-0.10127514
Sum49673.507
Variance49.187872
MonotonicityNot monotonic
2025-02-19T11:39:27.338000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.41 2
 
0.4%
106.908125 1
 
0.2%
96.63238095 1
 
0.2%
101.3555556 1
 
0.2%
92.42 1
 
0.2%
98.96642857 1
 
0.2%
103.450625 1
 
0.2%
109.7572222 1
 
0.2%
93.004375 1
 
0.2%
96.0584 1
 
0.2%
Other values (488) 488
97.8%
ValueCountFrequency (%)
79.43142857 1
0.2%
80.07 1
0.2%
80.087 1
0.2%
80.98388889 1
0.2%
82.99307692 1
0.2%
83.10466667 1
0.2%
83.3024 1
0.2%
83.494 1
0.2%
83.9975 1
0.2%
84.15722222 1
0.2%
ValueCountFrequency (%)
121.925625 1
0.2%
117.6672222 1
0.2%
117.1238095 1
0.2%
117.09 1
0.2%
115.9828571 1
0.2%
115.7063158 1
0.2%
114.6210526 1
0.2%
113.87 1
0.2%
113.4826667 1
0.2%
113.2035 1
0.2%

MEDIA_VIBR
Real number (ℝ)

Distinct491
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5353083
Minimum1.2018182
Maximum3.5094444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:27.573611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.2018182
5-th percentile1.9350641
Q12.3134375
median2.5365
Q32.7480833
95-th percentile3.1427611
Maximum3.5094444
Range2.3076263
Interquartile range (IQR)0.43464583

Descriptive statistics

Standard deviation0.34859625
Coefficient of variation (CV)0.13749659
Kurtosis0.18359433
Mean2.5353083
Median Absolute Deviation (MAD)0.21927778
Skewness-0.10064318
Sum1265.1188
Variance0.12151935
MonotonicityNot monotonic
2025-02-19T11:39:27.870739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.632307692 2
 
0.4%
2.962777778 2
 
0.4%
2.108125 2
 
0.4%
2.4925 2
 
0.4%
1.903333333 2
 
0.4%
2.647647059 2
 
0.4%
2.221 2
 
0.4%
2.500588235 2
 
0.4%
3.131818182 1
 
0.2%
2.734117647 1
 
0.2%
Other values (481) 481
96.4%
ValueCountFrequency (%)
1.201818182 1
0.2%
1.456666667 1
0.2%
1.61 1
0.2%
1.689285714 1
0.2%
1.734583333 1
0.2%
1.781666667 1
0.2%
1.783076923 1
0.2%
1.785263158 1
0.2%
1.798235294 1
0.2%
1.803333333 1
0.2%
ValueCountFrequency (%)
3.509444444 1
0.2%
3.338571429 1
0.2%
3.32 1
0.2%
3.316666667 1
0.2%
3.312352941 1
0.2%
3.298181818 1
0.2%
3.295882353 1
0.2%
3.290833333 1
0.2%
3.263571429 1
0.2%
3.256875 1
0.2%

MEDIA_HORAS_OPERATIVAS
Real number (ℝ)

Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50596.217
Minimum29094.318
Maximum68542.385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:28.118480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum29094.318
5-th percentile38817.546
Q146356.757
median50485.053
Q355287.713
95-th percentile62260.455
Maximum68542.385
Range39448.066
Interquartile range (IQR)8930.9557

Descriptive statistics

Standard deviation7143.2631
Coefficient of variation (CV)0.14118176
Kurtosis-0.021409756
Mean50596.217
Median Absolute Deviation (MAD)4468.7807
Skewness-0.14908071
Sum25247512
Variance51026208
MonotonicityNot monotonic
2025-02-19T11:39:28.376447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48090.3125 1
 
0.2%
38095.40909 1
 
0.2%
53136.11765 1
 
0.2%
50247.8125 1
 
0.2%
46299.80952 1
 
0.2%
66649.38889 1
 
0.2%
34110.81818 1
 
0.2%
34443.64286 1
 
0.2%
59488.375 1
 
0.2%
47633.11111 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
29094.31818 1
0.2%
29204.15789 1
0.2%
29316.66667 1
0.2%
32549.33333 1
0.2%
32557.5625 1
0.2%
32679.45455 1
0.2%
33131.73684 1
0.2%
33153.68 1
0.2%
34110.81818 1
0.2%
34153.75 1
0.2%
ValueCountFrequency (%)
68542.38462 1
0.2%
68210.66667 1
0.2%
67721.08333 1
0.2%
66649.38889 1
0.2%
65661.35714 1
0.2%
65036.35 1
0.2%
64699.0625 1
0.2%
64532.61538 1
0.2%
64526.83333 1
0.2%
64507.55556 1
0.2%

Vida_util_estimada
Real number (ℝ)

Distinct487
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94637.06
Minimum70524
Maximum99982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:28.647409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70524
5-th percentile85025.1
Q192276
median96218
Q398421
95-th percentile99751.9
Maximum99982
Range29458
Interquartile range (IQR)6145

Descriptive statistics

Standard deviation5040.6204
Coefficient of variation (CV)0.053262648
Kurtosis3.5753992
Mean94637.06
Median Absolute Deviation (MAD)2777
Skewness-1.6436326
Sum47223893
Variance25407854
MonotonicityNot monotonic
2025-02-19T11:39:28.905018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99333 2
 
0.4%
94216 2
 
0.4%
89711 2
 
0.4%
99935 2
 
0.4%
86426 2
 
0.4%
93986 2
 
0.4%
99079 2
 
0.4%
98035 2
 
0.4%
99861 2
 
0.4%
94122 2
 
0.4%
Other values (477) 479
96.0%
ValueCountFrequency (%)
70524 1
0.2%
70889 1
0.2%
71579 1
0.2%
72510 1
0.2%
75876 1
0.2%
77730 1
0.2%
78763 1
0.2%
79409 1
0.2%
79443 1
0.2%
79794 1
0.2%
ValueCountFrequency (%)
99982 1
0.2%
99939 1
0.2%
99935 2
0.4%
99933 1
0.2%
99905 1
0.2%
99898 1
0.2%
99890 1
0.2%
99881 1
0.2%
99874 1
0.2%
99872 1
0.2%

Total_Ordenes
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.93988
Minimum7
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:29.137542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q117
median20
Q323
95-th percentile28
Maximum36
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5654975
Coefficient of variation (CV)0.22896314
Kurtosis0.068828658
Mean19.93988
Median Absolute Deviation (MAD)3
Skewness0.31398371
Sum9950
Variance20.843768
MonotonicityNot monotonic
2025-02-19T11:39:29.337369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
18 47
 
9.4%
19 43
 
8.6%
21 39
 
7.8%
20 37
 
7.4%
17 37
 
7.4%
16 36
 
7.2%
24 36
 
7.2%
23 35
 
7.0%
15 34
 
6.8%
22 29
 
5.8%
Other values (19) 126
25.3%
ValueCountFrequency (%)
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 5
 
1.0%
12 10
 
2.0%
13 12
 
2.4%
14 19
3.8%
15 34
6.8%
16 36
7.2%
ValueCountFrequency (%)
36 1
 
0.2%
34 1
 
0.2%
33 1
 
0.2%
32 2
 
0.4%
31 3
 
0.6%
30 6
 
1.2%
29 6
 
1.2%
28 7
1.4%
27 12
2.4%
26 16
3.2%

Ordenes_Correctivo
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.138277
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:29.549857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q312
95-th percentile15.1
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2608974
Coefficient of variation (CV)0.32164218
Kurtosis-0.0018880075
Mean10.138277
Median Absolute Deviation (MAD)2
Skewness0.27670445
Sum5059
Variance10.633452
MonotonicityNot monotonic
2025-02-19T11:39:29.774314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10 59
11.8%
9 56
11.2%
11 56
11.2%
8 52
10.4%
12 49
9.8%
7 44
8.8%
13 41
8.2%
14 34
6.8%
6 28
5.6%
5 22
 
4.4%
Other values (10) 58
11.6%
ValueCountFrequency (%)
1 1
 
0.2%
3 1
 
0.2%
4 14
 
2.8%
5 22
 
4.4%
6 28
5.6%
7 44
8.8%
8 52
10.4%
9 56
11.2%
10 59
11.8%
11 56
11.2%
ValueCountFrequency (%)
21 1
 
0.2%
20 2
 
0.4%
19 3
 
0.6%
18 3
 
0.6%
17 7
 
1.4%
16 9
 
1.8%
15 17
 
3.4%
14 34
6.8%
13 41
8.2%
12 49
9.8%

Ordenes_Preventivo
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8016032
Minimum2
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:30.022717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q312
95-th percentile15
Maximum23
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1949253
Coefficient of variation (CV)0.32595946
Kurtosis0.3073908
Mean9.8016032
Median Absolute Deviation (MAD)2
Skewness0.26313678
Sum4891
Variance10.207548
MonotonicityNot monotonic
2025-02-19T11:39:30.206132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 72
14.4%
11 66
13.2%
9 54
10.8%
12 51
10.2%
8 50
10.0%
6 37
7.4%
7 36
7.2%
13 30
6.0%
5 26
 
5.2%
14 22
 
4.4%
Other values (9) 55
11.0%
ValueCountFrequency (%)
2 3
 
0.6%
3 4
 
0.8%
4 13
 
2.6%
5 26
 
5.2%
6 37
7.4%
7 36
7.2%
8 50
10.0%
9 54
10.8%
10 72
14.4%
11 66
13.2%
ValueCountFrequency (%)
23 1
 
0.2%
19 1
 
0.2%
18 5
 
1.0%
17 6
 
1.2%
16 11
 
2.2%
15 11
 
2.2%
14 22
 
4.4%
13 30
6.0%
12 51
10.2%
11 66
13.2%

DIAS_ENTRE_FALL_CONSEC
Real number (ℝ)

High correlation 

Distinct410
Distinct (%)82.3%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean40.007818
Minimum13
Maximum108.33333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:30.422145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile23.415993
Q129.5
median37
Q346.138393
95-th percentile69.6225
Maximum108.33333
Range95.333333
Interquartile range (IQR)16.638393

Descriptive statistics

Standard deviation14.685419
Coefficient of variation (CV)0.36706374
Kurtosis2.2360104
Mean40.007818
Median Absolute Deviation (MAD)8.25
Skewness1.3340498
Sum19923.893
Variance215.66154
MonotonicityNot monotonic
2025-02-19T11:39:30.661596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.33333333 4
 
0.8%
45 4
 
0.8%
43 4
 
0.8%
55 4
 
0.8%
42.375 3
 
0.6%
32.83333333 3
 
0.6%
33.18181818 3
 
0.6%
29.5 3
 
0.6%
27.92307692 3
 
0.6%
30 3
 
0.6%
Other values (400) 464
93.0%
ValueCountFrequency (%)
13 1
0.2%
16.9 1
0.2%
17.28571429 1
0.2%
17.38888889 1
0.2%
18.05 1
0.2%
19 1
0.2%
19.8125 1
0.2%
20 1
0.2%
20.05882353 1
0.2%
20.26315789 1
0.2%
ValueCountFrequency (%)
108.3333333 1
0.2%
94.75 1
0.2%
91 1
0.2%
89.5 1
0.2%
89.25 1
0.2%
89 1
0.2%
88.33333333 1
0.2%
87.66666667 1
0.2%
87.5 1
0.2%
83.25 1
0.2%

COSTE_MTO_CORR_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4971.7426
Minimum2071.4862
Maximum8159.0614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:30.899934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2071.4862
5-th percentile3254.5601
Q14399.4275
median5038.6933
Q35584.0271
95-th percentile6364.6025
Maximum8159.0614
Range6087.5752
Interquartile range (IQR)1184.5996

Descriptive statistics

Standard deviation952.53754
Coefficient of variation (CV)0.19159028
Kurtosis0.56598606
Mean4971.7426
Median Absolute Deviation (MAD)597.5098
Skewness-0.14429209
Sum2480899.5
Variance907327.77
MonotonicityNot monotonic
2025-02-19T11:39:31.142504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5284.873 1
 
0.2%
5268.128333 1
 
0.2%
5038.693333 1
 
0.2%
4940.70875 1
 
0.2%
2071.48625 1
 
0.2%
6046.212857 1
 
0.2%
4633.09125 1
 
0.2%
3019.073846 1
 
0.2%
4918.11125 1
 
0.2%
4805.495 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
2071.48625 1
0.2%
2092.154444 1
0.2%
2201.694 1
0.2%
2203.304444 1
0.2%
2637.113333 1
0.2%
2684.46375 1
0.2%
2787.2475 1
0.2%
2800.600909 1
0.2%
2855.385 1
0.2%
2869.835 1
0.2%
ValueCountFrequency (%)
8159.061429 1
0.2%
8071.452 1
0.2%
7789.38 1
0.2%
7582.626667 1
0.2%
7400.464545 1
0.2%
7388.197143 1
0.2%
7072.002222 1
0.2%
6935.055714 1
0.2%
6920.953333 1
0.2%
6904.836667 1
0.2%

DURACION_HORAS_CORR_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation 

Distinct374
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.137232
Minimum12.125
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:31.412274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12.125
5-th percentile16.564286
Q121.261364
median24.181818
Q327.132479
95-th percentile31.631731
Maximum38
Range25.875
Interquartile range (IQR)5.871115

Descriptive statistics

Standard deviation4.3819079
Coefficient of variation (CV)0.18154144
Kurtosis0.2307911
Mean24.137232
Median Absolute Deviation (MAD)2.9318182
Skewness0.11233437
Sum12044.479
Variance19.201117
MonotonicityNot monotonic
2025-02-19T11:39:31.681599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 6
 
1.2%
25 6
 
1.2%
24 6
 
1.2%
28 5
 
1.0%
22.25 5
 
1.0%
26 4
 
0.8%
23.5 4
 
0.8%
24.85714286 3
 
0.6%
22.5 3
 
0.6%
24.7 3
 
0.6%
Other values (364) 454
91.0%
ValueCountFrequency (%)
12.125 1
0.2%
12.33333333 1
0.2%
12.8 1
0.2%
13.28571429 1
0.2%
13.6 1
0.2%
13.90909091 1
0.2%
14.33333333 1
0.2%
15 1
0.2%
15.11111111 1
0.2%
15.30769231 1
0.2%
ValueCountFrequency (%)
38 1
0.2%
37.83333333 1
0.2%
37.28571429 1
0.2%
37 1
0.2%
36.14285714 1
0.2%
34.61538462 1
0.2%
33.92857143 1
0.2%
33.57142857 1
0.2%
33.25 1
0.2%
33.125 1
0.2%

COSTE_MTO_CORR_TOTAL
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50415.963
Minimum6095.01
Maximum114016.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:31.935995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6095.01
5-th percentile20617.521
Q137666.4
median49777.44
Q362699.015
95-th percentile83038.809
Maximum114016.5
Range107921.49
Interquartile range (IQR)25032.615

Descriptive statistics

Standard deviation18619.972
Coefficient of variation (CV)0.36932692
Kurtosis0.075492439
Mean50415.963
Median Absolute Deviation (MAD)12653.17
Skewness0.33735166
Sum25157565
Variance3.4670337 × 108
MonotonicityNot monotonic
2025-02-19T11:39:32.225948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52848.73 1
 
0.2%
63217.54 1
 
0.2%
30232.16 1
 
0.2%
39525.67 1
 
0.2%
16571.89 1
 
0.2%
42323.49 1
 
0.2%
37064.73 1
 
0.2%
39247.96 1
 
0.2%
39344.89 1
 
0.2%
38443.96 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
6095.01 1
0.2%
11008.47 1
0.2%
11148.99 1
0.2%
12395.74 1
0.2%
13060.32 1
0.2%
13551.47 1
0.2%
15436.74 1
0.2%
16571.89 1
0.2%
17132.31 1
0.2%
17219.01 1
0.2%
ValueCountFrequency (%)
114016.5 1
0.2%
113072.63 1
0.2%
111759.65 1
0.2%
99189.08 1
0.2%
97504.85 1
0.2%
96404.91 1
0.2%
95066.93 1
0.2%
94853.37 1
0.2%
93603.15 1
0.2%
92213.49 1
0.2%

DURACION_HORAS_CORR_TOTAL
Real number (ℝ)

High correlation 

Distinct264
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.998
Minimum37
Maximum554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:32.462080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile104.8
Q1183
median241
Q3298.5
95-th percentile405.2
Maximum554
Range517
Interquartile range (IQR)115.5

Descriptive statistics

Standard deviation90.919654
Coefficient of variation (CV)0.37110367
Kurtosis0.26970384
Mean244.998
Median Absolute Deviation (MAD)58
Skewness0.47200351
Sum122254
Variance8266.3835
MonotonicityNot monotonic
2025-02-19T11:39:32.713608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206 7
 
1.4%
247 6
 
1.2%
261 6
 
1.2%
229 6
 
1.2%
270 5
 
1.0%
267 5
 
1.0%
205 5
 
1.0%
227 5
 
1.0%
192 5
 
1.0%
204 5
 
1.0%
Other values (254) 444
89.0%
ValueCountFrequency (%)
37 1
0.2%
62 1
0.2%
65 1
0.2%
66 1
0.2%
67 1
0.2%
68 1
0.2%
74 1
0.2%
77 1
0.2%
79 1
0.2%
81 2
0.4%
ValueCountFrequency (%)
554 1
0.2%
552 1
0.2%
519 1
0.2%
506 1
0.2%
503 1
0.2%
490 1
0.2%
489 1
0.2%
484 1
0.2%
479 1
0.2%
477 1
0.2%

COSTE_MTO_PREV_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.04
Minimum2304.66
Maximum8376.205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:32.951901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2304.66
5-th percentile3571.0551
Q14358.9047
median5017.6033
Q35590.6985
95-th percentile6553.6086
Maximum8376.205
Range6071.545
Interquartile range (IQR)1231.7938

Descriptive statistics

Standard deviation908.85425
Coefficient of variation (CV)0.1817694
Kurtosis0.11024102
Mean5000.04
Median Absolute Deviation (MAD)617.34667
Skewness0.079228121
Sum2495019.9
Variance826016.05
MonotonicityNot monotonic
2025-02-19T11:39:33.189078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4448.922 1
 
0.2%
4721.025 1
 
0.2%
4790.019167 1
 
0.2%
5625.5575 1
 
0.2%
4509.798824 1
 
0.2%
4674.227273 1
 
0.2%
4025.075455 1
 
0.2%
5508.71 1
 
0.2%
6000.203846 1
 
0.2%
5720.027778 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
2304.66 1
0.2%
2393.338182 1
0.2%
2729.271667 1
0.2%
2857.472 1
0.2%
2922.681 1
0.2%
2928.785 1
0.2%
2954.012 1
0.2%
2967.164 1
0.2%
2973.8025 1
0.2%
3140.7675 1
0.2%
ValueCountFrequency (%)
8376.205 1
0.2%
7443.45875 1
0.2%
7170.754 1
0.2%
7077.63 1
0.2%
7058.525 1
0.2%
7019.98 1
0.2%
7019.018333 1
0.2%
7017.6175 1
0.2%
6922.748 1
0.2%
6884.00625 1
0.2%

DURACION_HORAS_PREV_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation 

Distinct365
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.003233
Minimum7.3333333
Maximum38.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:33.433128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7.3333333
5-th percentile17
Q120.69375
median24
Q327.055556
95-th percentile31.6025
Maximum38.2
Range30.866667
Interquartile range (IQR)6.3618056

Descriptive statistics

Standard deviation4.5376943
Coefficient of variation (CV)0.18904513
Kurtosis0.30083563
Mean24.003233
Median Absolute Deviation (MAD)3.2
Skewness0.016088001
Sum11977.613
Variance20.590669
MonotonicityNot monotonic
2025-02-19T11:39:33.669663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 9
 
1.8%
28 6
 
1.2%
19.6 5
 
1.0%
25.5 5
 
1.0%
19 5
 
1.0%
26 5
 
1.0%
25 5
 
1.0%
24 4
 
0.8%
20 4
 
0.8%
22 4
 
0.8%
Other values (355) 447
89.6%
ValueCountFrequency (%)
7.333333333 1
0.2%
11 1
0.2%
11.16666667 1
0.2%
11.54545455 1
0.2%
12.3 1
0.2%
14.2 1
0.2%
14.42857143 1
0.2%
14.54545455 1
0.2%
14.57142857 1
0.2%
14.625 1
0.2%
ValueCountFrequency (%)
38.2 1
0.2%
37 1
0.2%
35.66666667 1
0.2%
35.1 1
0.2%
34.75 1
0.2%
34.66666667 1
0.2%
34.6 1
0.2%
34.4 1
0.2%
34.375 1
0.2%
34.33333333 1
0.2%

COSTE_MTO_PREV_TOTAL
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48831.646
Minimum8651.53
Maximum130868.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:33.936790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8651.53
5-th percentile21337.635
Q135769.7
median49231.62
Q359911.78
95-th percentile78448.008
Maximum130868.53
Range122217
Interquartile range (IQR)24142.08

Descriptive statistics

Standard deviation17586.966
Coefficient of variation (CV)0.36015508
Kurtosis0.53488928
Mean48831.646
Median Absolute Deviation (MAD)11757.79
Skewness0.3602367
Sum24366991
Variance3.0930136 × 108
MonotonicityNot monotonic
2025-02-19T11:39:34.205916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44489.22 1
 
0.2%
56652.3 1
 
0.2%
57480.23 1
 
0.2%
45004.46 1
 
0.2%
76666.58 1
 
0.2%
51416.5 1
 
0.2%
44275.83 1
 
0.2%
60595.81 1
 
0.2%
78002.65 1
 
0.2%
102960.5 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
8651.53 1
0.2%
9218.64 1
0.2%
10351.48 1
0.2%
12169.87 1
0.2%
13686.22 1
0.2%
14287.36 1
0.2%
14770.06 1
0.2%
16214.51 1
0.2%
16375.63 1
0.2%
16402.3 1
0.2%
ValueCountFrequency (%)
130868.53 1
0.2%
102960.5 1
0.2%
98559.29 1
0.2%
96511.28 1
0.2%
94681.05 1
0.2%
94094.56 1
0.2%
90364.86 1
0.2%
89973.75 1
0.2%
89440.36 1
0.2%
87445.14 1
0.2%

DURACION_HORAS_PREV_TOTAL
Real number (ℝ)

High correlation 

Distinct265
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.64329
Minimum22
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:34.456900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile107
Q1173
median231
Q3285
95-th percentile382.2
Maximum511
Range489
Interquartile range (IQR)112

Descriptive statistics

Standard deviation86.266405
Coefficient of variation (CV)0.36764915
Kurtosis0.12412879
Mean234.64329
Median Absolute Deviation (MAD)55
Skewness0.41987507
Sum117087
Variance7441.8926
MonotonicityNot monotonic
2025-02-19T11:39:34.710248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 6
 
1.2%
209 5
 
1.0%
232 5
 
1.0%
243 5
 
1.0%
176 5
 
1.0%
274 5
 
1.0%
195 5
 
1.0%
239 5
 
1.0%
227 5
 
1.0%
249 5
 
1.0%
Other values (255) 448
89.8%
ValueCountFrequency (%)
22 2
0.4%
48 1
0.2%
52 1
0.2%
67 1
0.2%
68 1
0.2%
73 1
0.2%
78 1
0.2%
79 1
0.2%
87 1
0.2%
89 2
0.4%
ValueCountFrequency (%)
511 1
0.2%
501 1
0.2%
486 1
0.2%
481 1
0.2%
462 1
0.2%
459 1
0.2%
458 1
0.2%
453 2
0.4%
445 1
0.2%
442 1
0.2%

COSTE_MTO_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4985.4772
Minimum2498.3807
Maximum6956.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:34.958869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2498.3807
5-th percentile3900.6471
Q14618.4741
median4994.5767
Q35366.3089
95-th percentile6090.439
Maximum6956.17
Range4457.7893
Interquartile range (IQR)747.83483

Descriptive statistics

Standard deviation638.29316
Coefficient of variation (CV)0.12803051
Kurtosis0.49390074
Mean4985.4772
Median Absolute Deviation (MAD)375.00333
Skewness-0.073272285
Sum2487753.1
Variance407418.16
MonotonicityNot monotonic
2025-02-19T11:39:35.198505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4866.8975 1
 
0.2%
4994.576667 1
 
0.2%
4872.910556 1
 
0.2%
5283.133125 1
 
0.2%
3729.5388 1
 
0.2%
5207.777222 1
 
0.2%
4281.082105 1
 
0.2%
4160.157083 1
 
0.2%
5587.978095 1
 
0.2%
5438.633077 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
2498.380667 1
0.2%
2783.970667 1
0.2%
3356.644 1
0.2%
3385.696 1
0.2%
3527.9596 1
0.2%
3628.517826 1
0.2%
3634.188333 1
0.2%
3634.923077 1
0.2%
3654.544 1
0.2%
3679.5344 1
0.2%
ValueCountFrequency (%)
6956.17 1
0.2%
6790.311667 1
0.2%
6658.467857 1
0.2%
6602.345 1
0.2%
6513.377143 1
0.2%
6484.124375 1
0.2%
6481.694667 1
0.2%
6453.197222 1
0.2%
6388.24125 1
0.2%
6353.71 1
0.2%

DURACION_HORAS_MEDIO_POR_ORDEN
Real number (ℝ)

High correlation 

Distinct423
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.086804
Minimum14.4
Maximum34.461538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:35.443328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14.4
5-th percentile19.358523
Q122.123737
median24.1875
Q326
95-th percentile28.935652
Maximum34.461538
Range20.061538
Interquartile range (IQR)3.8762626

Descriptive statistics

Standard deviation2.972684
Coefficient of variation (CV)0.12341546
Kurtosis0.37775025
Mean24.086804
Median Absolute Deviation (MAD)1.9034091
Skewness0.082890538
Sum12019.315
Variance8.8368499
MonotonicityNot monotonic
2025-02-19T11:39:35.707386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 7
 
1.4%
24 5
 
1.0%
27 4
 
0.8%
24.8 4
 
0.8%
24.66666667 3
 
0.6%
23 3
 
0.6%
26.57142857 3
 
0.6%
22.25 3
 
0.6%
22 3
 
0.6%
20.33333333 3
 
0.6%
Other values (413) 461
92.4%
ValueCountFrequency (%)
14.4 1
0.2%
15.33333333 1
0.2%
15.8125 1
0.2%
17.13333333 1
0.2%
17.1875 1
0.2%
17.2 1
0.2%
17.40909091 1
0.2%
17.42857143 1
0.2%
17.61111111 1
0.2%
18.21052632 1
0.2%
ValueCountFrequency (%)
34.46153846 1
0.2%
33.94736842 1
0.2%
32.52941176 1
0.2%
32.23076923 1
0.2%
31.73333333 1
0.2%
31.59090909 1
0.2%
31.45454545 1
0.2%
30.5 1
0.2%
30.42105263 1
0.2%
30.375 1
0.2%

COSTE_MTO_TOTAL
Real number (ℝ)

High correlation  Unique 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99247.608
Minimum37475.71
Maximum187601.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:35.979706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum37475.71
5-th percentile60587.295
Q182437.415
median97251.23
Q3115037.84
95-th percentile144173.24
Maximum187601.77
Range150126.06
Interquartile range (IQR)32600.425

Descriptive statistics

Standard deviation25480.517
Coefficient of variation (CV)0.25673683
Kurtosis0.20504176
Mean99247.608
Median Absolute Deviation (MAD)16276.93
Skewness0.41776898
Sum49524557
Variance6.4925672 × 108
MonotonicityNot monotonic
2025-02-19T11:39:36.228532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97337.95 1
 
0.2%
119869.84 1
 
0.2%
87712.39 1
 
0.2%
84530.13 1
 
0.2%
93238.47 1
 
0.2%
93739.99 1
 
0.2%
81340.56 1
 
0.2%
99843.77 1
 
0.2%
117347.54 1
 
0.2%
141404.46 1
 
0.2%
Other values (489) 489
98.0%
ValueCountFrequency (%)
37475.71 1
0.2%
41036.89 1
0.2%
41280.32 1
0.2%
41759.56 1
0.2%
43995.31 1
0.2%
45538.64 1
0.2%
45593.64 1
0.2%
47254 1
0.2%
50349.66 1
0.2%
50785.44 1
0.2%
ValueCountFrequency (%)
187601.77 1
0.2%
183067.45 1
0.2%
171184.2 1
0.2%
165278.54 1
0.2%
164986.19 1
0.2%
163698.37 1
0.2%
162201.68 1
0.2%
161622.62 1
0.2%
161084.75 1
0.2%
160542.87 1
0.2%

DURACION_HORAS_TOTAL
Real number (ℝ)

High correlation 

Distinct314
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean479.64128
Minimum178
Maximum854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:36.521319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum178
5-th percentile295.9
Q1389
median471
Q3561.5
95-th percentile686.4
Maximum854
Range676
Interquartile range (IQR)172.5

Descriptive statistics

Standard deviation121.26151
Coefficient of variation (CV)0.25281709
Kurtosis-0.18382896
Mean479.64128
Median Absolute Deviation (MAD)86
Skewness0.26531629
Sum239341
Variance14704.355
MonotonicityNot monotonic
2025-02-19T11:39:36.798699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
620 6
 
1.2%
495 5
 
1.0%
534 5
 
1.0%
434 4
 
0.8%
456 4
 
0.8%
336 4
 
0.8%
394 4
 
0.8%
489 4
 
0.8%
544 4
 
0.8%
403 4
 
0.8%
Other values (304) 455
91.2%
ValueCountFrequency (%)
178 1
0.2%
184 1
0.2%
199 1
0.2%
214 1
0.2%
216 1
0.2%
231 1
0.2%
236 1
0.2%
243 1
0.2%
244 1
0.2%
253 1
0.2%
ValueCountFrequency (%)
854 1
0.2%
837 1
0.2%
819 1
0.2%
810 1
0.2%
803 1
0.2%
774 1
0.2%
756 1
0.2%
752 1
0.2%
744 1
0.2%
740 1
0.2%

Tipo_Equipo
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
Generador
135 
Transformador
125 
Compresor
121 
Motor
118 

Length

Max length13
Median length9
Mean length9.0561122
Min length5

Characters and Unicode

Total characters4519
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotor
2nd rowMotor
3rd rowMotor
4th rowTransformador
5th rowCompresor

Common Values

ValueCountFrequency (%)
Generador 135
27.1%
Transformador 125
25.1%
Compresor 121
24.2%
Motor 118
23.6%

Length

2025-02-19T11:39:37.171413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T11:39:37.474267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
generador 135
27.1%
transformador 125
25.1%
compresor 121
24.2%
motor 118
23.6%

Most occurring characters

ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1005
22.2%
o 863
19.1%
e 391
 
8.7%
a 385
 
8.5%
n 260
 
5.8%
d 260
 
5.8%
s 246
 
5.4%
m 246
 
5.4%
G 135
 
3.0%
T 125
 
2.8%
Other values (5) 603
13.3%

Fabricante
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
ABB
131 
Siemens
129 
GE
123 
Schneider
116 

Length

Max length9
Median length7
Mean length5.1823647
Min length2

Characters and Unicode

Total characters2586
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSchneider
2nd rowABB
3rd rowGE
4th rowSiemens
5th rowABB

Common Values

ValueCountFrequency (%)
ABB 131
26.3%
Siemens 129
25.9%
GE 123
24.6%
Schneider 116
23.2%

Length

2025-02-19T11:39:37.679916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T11:39:37.908752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
abb 131
26.3%
siemens 129
25.9%
ge 123
24.6%
schneider 116
23.2%

Most occurring characters

ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 490
18.9%
B 262
10.1%
S 245
9.5%
i 245
9.5%
n 245
9.5%
A 131
 
5.1%
m 129
 
5.0%
s 129
 
5.0%
G 123
 
4.8%
E 123
 
4.8%
Other values (4) 464
17.9%

Modelo
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size831.0 B
Z300
141 
Y200
126 
X100
121 
M400
111 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1996
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX100
2nd rowZ300
3rd rowY200
4th rowX100
5th rowY200

Common Values

ValueCountFrequency (%)
Z300 141
28.3%
Y200 126
25.3%
X100 121
24.2%
M400 111
22.2%

Length

2025-02-19T11:39:38.095594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T11:39:38.338462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
z300 141
28.3%
y200 126
25.3%
x100 121
24.2%
m400 111
22.2%

Most occurring characters

ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 998
50.0%
Z 141
 
7.1%
3 141
 
7.1%
Y 126
 
6.3%
2 126
 
6.3%
X 121
 
6.1%
1 121
 
6.1%
M 111
 
5.6%
4 111
 
5.6%

Potencia_kW
Real number (ℝ)

Distinct471
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2420.6553
Minimum-100
Maximum4997
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)2.0%
Memory size4.0 KiB
2025-02-19T11:39:38.731807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile249.8
Q11233
median2406
Q33563.5
95-th percentile4786.1
Maximum4997
Range5097
Interquartile range (IQR)2330.5

Descriptive statistics

Standard deviation1443.2171
Coefficient of variation (CV)0.59620927
Kurtosis-1.1039458
Mean2420.6553
Median Absolute Deviation (MAD)1168
Skewness0.054996689
Sum1207907
Variance2082875.7
MonotonicityNot monotonic
2025-02-19T11:39:39.218697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 10
 
2.0%
562 3
 
0.6%
799 2
 
0.4%
1536 2
 
0.4%
670 2
 
0.4%
4948 2
 
0.4%
3212 2
 
0.4%
404 2
 
0.4%
1514 2
 
0.4%
4080 2
 
0.4%
Other values (461) 470
94.2%
ValueCountFrequency (%)
-100 10
2.0%
53 1
 
0.2%
79 1
 
0.2%
137 1
 
0.2%
146 1
 
0.2%
153 1
 
0.2%
163 1
 
0.2%
173 1
 
0.2%
187 1
 
0.2%
197 1
 
0.2%
ValueCountFrequency (%)
4997 1
0.2%
4993 1
0.2%
4992 1
0.2%
4974 1
0.2%
4948 2
0.4%
4947 1
0.2%
4945 1
0.2%
4930 1
0.2%
4929 1
0.2%
4914 1
0.2%
Distinct489
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.1603
Minimum525
Maximum9993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-02-19T11:39:39.536864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile855.5
Q12896
median5258
Q37451.5
95-th percentile9478.4
Maximum9993
Range9468
Interquartile range (IQR)4555.5

Descriptive statistics

Standard deviation2744.958
Coefficient of variation (CV)0.5253347
Kurtosis-1.1560925
Mean5225.1603
Median Absolute Deviation (MAD)2259
Skewness-0.029294541
Sum2607355
Variance7534794.5
MonotonicityNot monotonic
2025-02-19T11:39:39.940616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
655 2
 
0.4%
1783 2
 
0.4%
2270 2
 
0.4%
8101 2
 
0.4%
2386 2
 
0.4%
9026 2
 
0.4%
1717 2
 
0.4%
6992 2
 
0.4%
5938 2
 
0.4%
1534 2
 
0.4%
Other values (479) 479
96.0%
ValueCountFrequency (%)
525 1
0.2%
549 1
0.2%
564 1
0.2%
572 1
0.2%
585 1
0.2%
618 1
0.2%
654 1
0.2%
655 2
0.4%
661 1
0.2%
663 1
0.2%
ValueCountFrequency (%)
9993 1
0.2%
9933 1
0.2%
9926 1
0.2%
9921 1
0.2%
9916 1
0.2%
9909 1
0.2%
9881 1
0.2%
9851 1
0.2%
9836 1
0.2%
9829 1
0.2%

Interactions

2025-02-19T11:39:19.654611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:26.814014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:31.722053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:36.543857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:41.667083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:46.953274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:51.835115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:57.652483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:03.448636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:08.681120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:14.533979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:19.541680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:24.354966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:29.352462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:34.749904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:39.812658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:44.786149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:49.608828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:55.086216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:59.734119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:04.838180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:09.803270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:14.565269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:19.864036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:27.012620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:31.906005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:36.744387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:41.886804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:47.158583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:52.067686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:57.982809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:03.644736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:08.897150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:14.750581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:19.736923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:24.557246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:29.560396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:34.950753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:40.031222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:45.022853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:49.824418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:55.281865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:59.953886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:05.042407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:10.000737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:14.759497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:20.058538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:27.195208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:32.086840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:36.952762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:42.111167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:47.356542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:52.256831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:58.194605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:03.843684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:09.115474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:14.954505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:19.937483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:24.766533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:29.766575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2025-02-19T11:39:24.545744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:31.123264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:35.949155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:40.897158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:46.323563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:51.131404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:56.679594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:02.668222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:08.068221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:13.446013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:18.930041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:23.737638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:28.427683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:34.154704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:39.214138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:44.070147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:48.973383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:53.778592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:59.140913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:04.155371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:09.191797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:13.965423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:19.044218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:24.748996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:31.327116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:36.156504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:41.122885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:46.536420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:51.342309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:56.885876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:03.017623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:08.277594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:14.135817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:19.141142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:23.959012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:28.643110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:34.357794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:39.419164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:44.371702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:49.215539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:53.981911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:59.345678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:04.414241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:09.403361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:14.167728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:19.244607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:24.951880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:31.526078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:36.351032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:41.395062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:46.741428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:51.544992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:37:57.443671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:03.245319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:08.479514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:14.338217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:19.341842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:24.158762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:29.021145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:34.557801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:39.617756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:44.588563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:49.414281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:54.879092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:38:59.543123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:04.622918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:09.605877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:14.369775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-02-19T11:39:19.458620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2025-02-19T11:39:40.281719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
COSTE_MTO_CORR_MEDIO_POR_ORDENCOSTE_MTO_CORR_TOTALCOSTE_MTO_MEDIO_POR_ORDENCOSTE_MTO_PREV_MEDIO_POR_ORDENCOSTE_MTO_PREV_TOTALCOSTE_MTO_TOTALDIAS_ENTRE_FALL_CONSECDURACION_HORAS_CORR_MEDIO_POR_ORDENDURACION_HORAS_CORR_TOTALDURACION_HORAS_MEDIO_POR_ORDENDURACION_HORAS_PREV_MEDIO_POR_ORDENDURACION_HORAS_PREV_TOTALDURACION_HORAS_TOTALFabricanteHoras_Recomendadas_RevisionID_EquipoMEDIA_HORAS_OPERATIVASMEDIA_TEMPMEDIA_VIBRModeloOrdenes_CorrectivoOrdenes_PreventivoPotencia_kWTipo_EquipoTotal_OrdenesVida_util_estimada
COSTE_MTO_CORR_MEDIO_POR_ORDEN1.0000.4300.6730.003-0.0400.300-0.0040.0810.0180.0550.019-0.018-0.0120.0540.0130.017-0.019-0.022-0.0100.034-0.014-0.0320.0070.050-0.0330.003
COSTE_MTO_CORR_TOTAL0.4301.0000.313-0.012-0.0250.708-0.7570.0400.7830.013-0.020-0.0350.5390.0580.0240.006-0.069-0.0280.0060.0000.872-0.014-0.0540.0850.600-0.011
COSTE_MTO_MEDIO_POR_ORDEN0.6730.3131.0000.6720.2590.415-0.0060.0930.0320.0540.015-0.041-0.0220.0000.0020.0100.035-0.002-0.0050.000-0.003-0.0670.0370.068-0.0540.013
COSTE_MTO_PREV_MEDIO_POR_ORDEN0.003-0.0120.6721.0000.4170.2710.0090.0620.0140.003-0.030-0.042-0.0300.000-0.0300.0060.0780.021-0.0180.000-0.005-0.0450.0910.000-0.0440.057
COSTE_MTO_PREV_TOTAL-0.040-0.0250.2590.4171.0000.651-0.0100.004-0.005-0.046-0.0350.7520.5250.000-0.0150.0090.0690.0460.0220.043-0.0070.8590.0350.0250.5880.063
COSTE_MTO_TOTAL0.3000.7080.4150.2710.6511.000-0.5610.0400.566-0.015-0.0370.4940.7720.0000.0010.0100.0040.0160.0020.0000.6280.576-0.0110.0000.8630.043
DIAS_ENTRE_FALL_CONSEC-0.004-0.757-0.0060.009-0.010-0.5611.000-0.049-0.758-0.0070.0230.002-0.5520.0000.0410.0050.0840.0040.0220.099-0.864-0.0210.0360.000-0.613-0.019
DURACION_HORAS_CORR_MEDIO_POR_ORDEN0.0810.0400.0930.0620.0040.040-0.0491.0000.4510.662-0.059-0.0420.3010.0000.010-0.0440.004-0.0850.0100.0750.024-0.0290.0010.061-0.0010.049
DURACION_HORAS_CORR_TOTAL0.0180.7830.0320.014-0.0050.566-0.7580.4511.0000.299-0.049-0.0420.6880.0000.016-0.020-0.055-0.062-0.0010.0240.881-0.010-0.0610.0000.6120.009
DURACION_HORAS_MEDIO_POR_ORDEN0.0550.0130.0540.003-0.046-0.015-0.0070.6620.2991.0000.6390.2620.4110.0690.017-0.0460.002-0.042-0.0240.0000.000-0.049-0.0640.082-0.0280.061
DURACION_HORAS_PREV_MEDIO_POR_ORDEN0.019-0.0200.015-0.030-0.035-0.0370.023-0.059-0.0490.6391.0000.4190.2560.0660.032-0.0140.0170.038-0.0270.000-0.021-0.026-0.0730.000-0.0330.041
DURACION_HORAS_PREV_TOTAL-0.018-0.035-0.041-0.0420.7520.4940.002-0.042-0.0420.2620.4191.0000.6600.0820.0070.0020.0310.0520.0170.000-0.0250.871-0.0140.0860.5840.057
DURACION_HORAS_TOTAL-0.0120.539-0.022-0.0300.5250.772-0.5520.3010.6880.4110.2560.6601.0000.0000.021-0.005-0.018-0.0120.0040.0000.6220.603-0.0600.0000.8830.048
Fabricante0.0540.0580.0000.0000.0000.0000.0000.0000.0000.0690.0660.0820.0001.0000.0600.0870.0000.1020.0870.0320.0000.0800.0350.0760.0000.065
Horas_Recomendadas_Revision0.0130.0240.002-0.030-0.0150.0010.0410.0100.0160.0170.0320.0070.0210.0601.0000.016-0.024-0.0700.0010.0000.0060.0050.0830.0000.013-0.040
ID_Equipo0.0170.0060.0100.0060.0090.0100.005-0.044-0.020-0.046-0.0140.002-0.0050.0870.0161.0000.0780.044-0.0100.051-0.0120.0130.0040.0000.009-0.065
MEDIA_HORAS_OPERATIVAS-0.019-0.0690.0350.0780.0690.0040.0840.004-0.0550.0020.0170.031-0.0180.000-0.0240.0781.0000.0390.0520.000-0.0550.0280.0190.000-0.0320.381
MEDIA_TEMP-0.022-0.028-0.0020.0210.0460.0160.004-0.085-0.062-0.0420.0380.052-0.0120.102-0.0700.0440.0391.0000.0630.000-0.0220.046-0.0510.0000.019-0.034
MEDIA_VIBR-0.0100.006-0.005-0.0180.0220.0020.0220.010-0.001-0.024-0.0270.0170.0040.0870.001-0.0100.0520.0631.0000.0000.0120.0320.0530.0000.0170.024
Modelo0.0340.0000.0000.0000.0430.0000.0990.0750.0240.0000.0000.0000.0000.0320.0000.0510.0000.0000.0001.0000.0000.0000.0000.0000.0180.096
Ordenes_Correctivo-0.0140.872-0.003-0.005-0.0070.628-0.8640.0240.8810.000-0.021-0.0250.6220.0000.006-0.012-0.055-0.0220.0120.0001.0000.001-0.0520.0000.6980.003
Ordenes_Preventivo-0.032-0.014-0.067-0.0450.8590.576-0.021-0.029-0.010-0.049-0.0260.8710.6030.0800.0050.0130.0280.0460.0320.0000.0011.0000.0040.0700.6840.046
Potencia_kW0.007-0.0540.0370.0910.035-0.0110.0360.001-0.061-0.064-0.073-0.014-0.0600.0350.0830.0040.019-0.0510.0530.000-0.0520.0041.0000.000-0.041-0.056
Tipo_Equipo0.0500.0850.0680.0000.0250.0000.0000.0610.0000.0820.0000.0860.0000.0760.0000.0000.0000.0000.0000.0000.0000.0700.0001.0000.0000.000
Total_Ordenes-0.0330.600-0.054-0.0440.5880.863-0.613-0.0010.612-0.028-0.0330.5840.8830.0000.0130.009-0.0320.0190.0170.0180.6980.684-0.0410.0001.0000.020
Vida_util_estimada0.003-0.0110.0130.0570.0630.043-0.0190.0490.0090.0610.0410.0570.0480.065-0.040-0.0650.381-0.0340.0240.0960.0030.046-0.0560.0000.0201.000

Missing values

2025-02-19T11:39:25.293882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-19T11:39:26.018955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_EquipoMEDIA_TEMPMEDIA_VIBRMEDIA_HORAS_OPERATIVASVida_util_estimadaTotal_OrdenesOrdenes_CorrectivoOrdenes_PreventivoDIAS_ENTRE_FALL_CONSECCOSTE_MTO_CORR_MEDIO_POR_ORDENDURACION_HORAS_CORR_MEDIO_POR_ORDENCOSTE_MTO_CORR_TOTALDURACION_HORAS_CORR_TOTALCOSTE_MTO_PREV_MEDIO_POR_ORDENDURACION_HORAS_PREV_MEDIO_POR_ORDENCOSTE_MTO_PREV_TOTALDURACION_HORAS_PREV_TOTALCOSTE_MTO_MEDIO_POR_ORDENDURACION_HORAS_MEDIO_POR_ORDENCOSTE_MTO_TOTALDURACION_HORAS_TOTALTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_Revision
01106.9081252.34625048090.31250092645.020101039.1111115284.87300015.40000052848.731544448.92200028.00000044489.222804866.89750021.70000097337.95434MotorSchneiderX10010099656
1295.3100002.40076964532.61538591899.0137655.3333333268.53428616.85714322879.741184062.37666729.66666724374.261783634.92307722.76923147254.00296MotorABBZ30012202165
2393.9227783.23222242117.11111197734.01610641.6666672923.60200012.80000029236.021285017.60333320.83333330105.621253708.85250015.81250059341.64253MotorGEY20037336674
3490.8815002.27850056715.35000093640.02012834.4545456895.78250023.50000082749.392824553.52250022.12500036428.181775958.87850022.950000119177.57459TransformadorSiemensX1006621480
45107.7146432.69321454120.14285799898.01710738.0000004787.36500024.40000047873.652444909.54857119.71428634366.841384837.67588222.47058882240.49382CompresorABBY2009824282
5693.0856522.54782646456.56521784467.024131126.0000005577.17307725.00000072503.253255073.61636424.90909155809.782745346.37625024.958333128313.03599CompresorGEZ30044268747
67109.9160001.82333339585.46666793947.0115645.7500005959.95200025.00000029799.761254230.84333330.33333325385.061825016.80181827.90909155184.82307TransformadorABBY20018301238
7883.3024002.36680043344.60000093045.027161125.6000005336.81312522.31250085389.013575401.18272717.63636459413.011945363.03777820.407407144802.02551TransformadorGEY2005323846
8995.3461542.27076941900.30769279443.01612425.7272735697.59000021.75000068371.082615145.09000023.75000020580.36955559.46500022.25000088951.44356CompresorABBX10034849688
91098.5454172.47000046106.12500098758.01810830.1111113927.38700025.40000039273.872545325.57875020.12500042604.631614548.80555623.05555681878.50415CompresorSchneiderM40032275305
ID_EquipoMEDIA_TEMPMEDIA_VIBRMEDIA_HORAS_OPERATIVASVida_util_estimadaTotal_OrdenesOrdenes_CorrectivoOrdenes_PreventivoDIAS_ENTRE_FALL_CONSECCOSTE_MTO_CORR_MEDIO_POR_ORDENDURACION_HORAS_CORR_MEDIO_POR_ORDENCOSTE_MTO_CORR_TOTALDURACION_HORAS_CORR_TOTALCOSTE_MTO_PREV_MEDIO_POR_ORDENDURACION_HORAS_PREV_MEDIO_POR_ORDENCOSTE_MTO_PREV_TOTALDURACION_HORAS_PREV_TOTALCOSTE_MTO_MEDIO_POR_ORDENDURACION_HORAS_MEDIO_POR_ORDENCOSTE_MTO_TOTALDURACION_HORAS_TOTALTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_Revision
489490102.0909091.20181856100.09090992265.01441058.3333333859.18500020.25000015436.74816009.85100026.30000060098.512635395.37500024.57142975535.25344TransformadorSiemensX100-1005118
490491103.7945452.21545560025.81818295653.01913626.4166675008.46307727.46153865110.023574007.68833323.00000024046.131384692.42894726.05263289156.15495TransformadorSiemensZ30035075258
491492101.4881252.07312560000.43750094216.02214823.9230776305.43142924.64285788276.043453140.76750027.75000025126.142225154.64454525.772727113402.18567GeneradorSchneiderM4002503083
49249396.2947622.90381060489.52381099027.0189943.8750005502.46000025.44444449522.142293488.31777817.77777831394.861604495.38888921.61111180917.00389TransformadorSiemensZ30028897172
493494107.5030432.85565252108.30434894081.0158752.5714293804.94750021.87500030439.581754055.31857123.57142928387.231653921.78733322.66666758826.81340TransformadorGEZ3001875331
494495105.3889472.30894754310.42105398566.023121129.9090915908.39083321.58333370900.692593985.03909126.63636443835.432934988.52695724.000000114736.12552CompresorSchneiderY20043836188
49549691.5987502.84500046477.54166790614.021101140.0000005850.82700029.30000058508.272935001.80818216.45454555019.891815406.10285722.571429113528.16474MotorSiemensX10036175594
496497114.6210532.64210553482.94736896443.022111135.7000004997.46545521.27272754972.122344917.13909126.63636454088.532934957.30227323.954545109060.65527GeneradorSchneiderY2005488614
497498100.6033332.57266756706.86666791463.021101133.8888895395.42600024.60000053954.262465679.83090922.54545562478.142485544.40000023.523810116432.40494MotorSchneiderM40026025874
49849995.4100002.76100061050.00000098945.01541188.3333335156.39000033.25000020625.561335076.35090931.18181855839.863435097.69466731.73333376465.42476MotorABBM4008776907